import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							a				(     UpperCAmelCase__			):







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   str:
        super().__init__()
        self.register_modules(vqvae=_a						,							unet=_a						,							scheduler=_a				)



 @torch.no_grad()
 def __call__(   self						,							lowerCAmelCase_ = 1						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = 0.0						,							lowerCAmelCase_ = 50						,							lowerCAmelCase_ = "pil"						,							lowerCAmelCase_ = True						,							**lowerCAmelCase_						,							)    ->   Optional[Any]:
        _A	  =    randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,							generator=_a						,							)
        _A	  =    latents.to(self.device				)

        # scale the initial noise by the standard deviation required by the scheduler
        _A	  =    latents * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(_a				)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        _A	  =    """eta""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)

        _A	  =    {}
        if accepts_eta:
               _A	  =    eta

        for t in self.progress_bar(self.scheduler.timesteps				):
               _A	  =    self.scheduler.scale_model_input(_a						,							_a				)
               # predict the noise residual
               _A	  =    self.unet(_a						,							_a				).sample
               # compute the previous noisy sample x_t -> x_t-1
               _A	  =    self.scheduler.step(_a						,							_a						,							_a						,							**_a				).prev_sample

        # decode the image latents with the VAE
        _A	  =    self.vqvae.decode(_a				).sample

        _A	  =    (image / 2 + 0.5).clamp(0						,							1				)
        _A	  =    image.cpu().permute(0						,							2						,							3						,							1				).numpy()
        if output_type == "pil":
               _A	  =    self.numpy_to_pil(_a				)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=_a				)










"""simple docstring"""




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase				:     List[str]					=				object()

# For specifying empty leaf dict `{}`
lowerCAmelCase				:     Any					=				object()



def        a__					(							snake_case__			,					snake_case__							)		->   List[Any]:
			lowerCamelCase      =   tuple((re.compile(x + """$"""							) for x in qs)							)
			for i in range(len(snake_case__							) - len(snake_case__							) + 1							):
						lowerCamelCase      =   [x.match(snake_case__							) for x, y in zip(snake_case__			,					ks[i:]							)]
						if matches and all(snake_case__							):
									return True
			return False



def        a__					(							snake_case__							)		->   str:
			def replace(snake_case__			,					snake_case__							):
						for rule, replacement in rules:
									if _match(snake_case__			,					snake_case__							):
												return replacement
						return val

			return replace



def        a__					(							)		->   Union[str, Any]:
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp"""			,					snake_case__							)),
			    (("transformer", "wte", "embedding"), P("""mp"""			,					snake_case__							)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("attention", "out_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("mlp", "c_fc", "bias"), P("""mp"""							)),
			    (("mlp", "c_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]



def        a__					(							snake_case__							)		->   Optional[Any]:
			lowerCamelCase      =   _get_partition_rules()
			lowerCamelCase      =   _replacement_rules(snake_case__							)
			lowerCamelCase      =   {k: _unmatched for k in flatten_dict(snake_case__							)}
			lowerCamelCase      =   {k: replace(snake_case__			,					snake_case__							) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(snake_case__							)							)



